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SVEMnet (version 3.2.0)

Self-Validated Ensemble Models with Lasso and Relaxed Elastic Net Regression

Description

Tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) ) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via 'glmnet' (Friedman et al. (2010) ). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) ). Package code was drafted with assistance from generative AI tools.

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Version

Install

install.packages('SVEMnet')

Monthly Downloads

223

Version

3.2.0

License

GPL-2 | GPL-3

Maintainer

Andrew T Karl

Last Published

January 23rd, 2026

Functions in SVEMnet (3.2.0)

glmnet_with_cv

Fit a glmnet Model with Repeated Cross-Validation
plot.svem_binomial

Plot Method for SVEM Binomial Models
bigexp_formula

Construct a formula for a new response using a bigexp_spec
bigexp_prepare

Prepare data to match a bigexp_spec
coef.svem_model

Coefficients for SVEM Models
bigexp_train

Build a spec and prepare training data in one call
bigexp_terms

Create a deterministic expansion spec for wide polynomial and interaction models
SVEMnet-package

SVEMnet: Self-Validated Ensemble Models with Relaxed Lasso and Elastic-Net Regression
SVEMnet

Fit an SVEMnet model (Self-Validated Ensemble Elastic Net)
lipid_screen

Lipid formulation screening data
plot.svem_model

Plot Method for SVEM Models (Gaussian / Generic)
print.bigexp_spec

Print method for bigexp_spec objects
svem_score_random

Random-search scoring for SVEM models
plot.svem_significance_test

Plot SVEM significance test results for one or more responses
predict_cv

Predict from glmnet_with_cv Fits (svem_cv Objects)
svem_export_candidates_csv

Export SVEM candidate sets to CSV
svem_random_table_multi

Generate a Random Prediction Table from Multiple SVEMnet Models (no refit)
print.svem_significance_test

Print Method for SVEM Significance Test
svem_nonzero

Coefficient Nonzero Percentages (SVEM)
predict.svem_model

Predict Method for SVEM Models (Gaussian and Binomial)
svem_wmt_multi

Whole-model tests for multiple SVEM responses (WMT wrapper)
with_bigexp_contrasts

Evaluate code with the spec's recorded contrast options
svem_select_from_score_table

Select best row and diverse candidates from an SVEM score table
svem_significance_test_parallel

SVEM whole-model significance test with mixture support (parallel)